The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed from modern machines such as Medical Resonance Imaging (MRI) scanners, Computed Tomographic (CT) scanners, and Positron Emission Tomographic (PET) scanners, to multimodality imaging systems such as PET-CT and PET-MRI systems. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical abnormalities such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan, it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition.
Many diseases may be diagnosed by observing fat or iron deposition in tissues or organs, as compared to the normal status. For example, hepatic steatosis is a critical indication of various liver diseases, such as nonalcoholic fatty liver disease (NAFLD). Increased iron deposition is observed to be associated with chronic viral hepatitis, alcoholic liver disease and nonalcoholic steatohepatitis. Other clinical applications include diagnosis of bone marrow diseases, quantitative characterization of adrenal masses, and quantification of fat and iron deposition in the heart. Therefore, rapid and accurate evaluation of fat or iron deposition in tissues or organs is of great clinical interest.
Magnetic resonance imaging (MRI) has been used for this purpose. Dixon imaging is a well-established MRI method to measure fat. The method uses image acquisitions with distinct fat-water phase differences to separate fat and water images. To facilitate fat evaluation, a fat percentage (FP) map can be generated. The FP map is defined by the value of fat/(water+fat) for each pixel or voxel in the image. Sources of field inhomogeneity, T2/T2* and T1 effects, if not considered and corrected in the design of the method, may degrade measurement accuracy, because signal FP is actually measured instead of proton density FP.
Measuring iron is usually accomplished by measuring the tissue transverse relaxation values (T2 or T2*) or relaxation rates (R2=1/T2 or R2*=1/T2*) with MRI, because iron deposition has been demonstrated to be closely related to T2/T2* or R2/R2*. This is generally accomplished by acquiring multi-echo data and then performing a log-linear fitting. However, previous studies have shown that in the presence of fat, directly measuring T2/T2* or R2/R2* using this kind of approach can be problematic due to the influence of fat.
Some efforts have been made to quantify or separate water and fat in images acquired at two or more echo times. However, even when water and fat are successfully separated, the correct identification of water and fat for the two separated quantities is challenging. Typically, some prior knowledge of the scanned tissue or subject and some assumptions are needed to help identify the water and fat. For example, in some methods, a seed point needs to be determined by some algorithm to be a fat dominated pixel or voxel, so that subsequent steps can use the information of this seed point to identify water and fat voxels. If the seed point selection algorithm fails (i.e., a water dominated voxel is selected), then the resultant water and fat images will be erroneously swapped. As another example, most methods assume phase error due to field inhomogeneity (i.e., field map) is smooth, and the identification of water and fat is based on such key assumption. However, if the phase error is not smooth, such as in areas with severe susceptibilities, then this would lead to water and fat swap in those areas.